PUB - Publikationen an der Universität Bielefeld

This paper’s main contribution is a Bayesian hierarchical grounding state prediction model implemented in an adaptive explainer agent assisting users with analogical problem-solving. This model lets the agent adapt dialogue moves regarding previously unmentioned domain entities that are similar to the ones already explained when they are instances of the same generalised schema in different domains. Learning such schemata facilitates knowledge transfer between domains and plays an important role in analogical reasoning. An explainer agent should be able to predict to what extent the explainee has learned to induce a schema in order to build up on this in the explanation process and make it more cooperative. This paper describes the approach of hierarchical grounding state prediction, introduces the analogy-based explanation generation process and the agent architecture implemented for this approach, as well as provides some example interactions as the first developers’ evaluation of the system in preparation for upcoming empirical studies.